Tiago K Colicchio1, Julio C Facelli2, Guilherme Del Fiol2, Debra L Scammon3, Watson A Bowes4, Scott P Narus4. 1. Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA. Electronic address: tiago.colicchio@utah.edu. 2. Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA. 3. Department of Marketing, David Eccles School of Business, University of Utah, Salt Lake City, UT, USA. 4. Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; Medical Informatics, Intermountain Healthcare, Salt Lake City, UT, USA.
Abstract
OBJECTIVE: To classify and characterize the variables commonly used to measure the impact of Information Technology (IT) adoption in health care, as well as settings and IT interventions tested, and to guide future research. MATERIALS AND METHODS: We conducted a descriptive study screening a sample of 236 studies from a previous systematic review to identify outcome measures used and the availability of data to calculate these measures. We also developed a taxonomy of commonly used measures and explored setting characteristics and IT interventions. RESULTS: Clinical decision support is the most common intervention tested, primarily in non-hospital-based clinics and large academic hospitals. We identified 15 taxa representing the 79 most commonly used measures. Quality of care was the most common category of these measurements with 62 instances, followed by productivity (11 instances) and patient safety (6 instances). Measures used varied according to type of setting, IT intervention and targeted population. DISCUSSION: This study provides an inventory and a taxonomy of commonly used measures that will help researchers select measures in future studies as well as identify gaps in their measurement approaches. The classification of the other protocol components such as settings and interventions will also help researchers identify underexplored areas of research on the impact of IT interventions in health care. CONCLUSION: A more robust and standardized measurement system and more detailed descriptions of interventions and settings are necessary to enable comparison between studies and a better understanding of the impact of IT adoption in health care settings.
OBJECTIVE: To classify and characterize the variables commonly used to measure the impact of Information Technology (IT) adoption in health care, as well as settings and IT interventions tested, and to guide future research. MATERIALS AND METHODS: We conducted a descriptive study screening a sample of 236 studies from a previous systematic review to identify outcome measures used and the availability of data to calculate these measures. We also developed a taxonomy of commonly used measures and explored setting characteristics and IT interventions. RESULTS: Clinical decision support is the most common intervention tested, primarily in non-hospital-based clinics and large academic hospitals. We identified 15 taxa representing the 79 most commonly used measures. Quality of care was the most common category of these measurements with 62 instances, followed by productivity (11 instances) and patient safety (6 instances). Measures used varied according to type of setting, IT intervention and targeted population. DISCUSSION: This study provides an inventory and a taxonomy of commonly used measures that will help researchers select measures in future studies as well as identify gaps in their measurement approaches. The classification of the other protocol components such as settings and interventions will also help researchers identify underexplored areas of research on the impact of IT interventions in health care. CONCLUSION: A more robust and standardized measurement system and more detailed descriptions of interventions and settings are necessary to enable comparison between studies and a better understanding of the impact of IT adoption in health care settings.
Authors: Tiago K Colicchio; Damian Borbolla; Vanessa D Colicchio; Debra L Scammon; Guilherme Del Fiol; Julio C Facelli; Watson A Bowes; Scott P Narus Journal: EGEMS (Wash DC) Date: 2019-05-06
Authors: Philip J Scott; Angela W Brown; Taiwo Adedeji; Jeremy C Wyatt; Andrew Georgiou; Eric L Eisenstein; Charles P Friedman Journal: J Am Med Inform Assoc Date: 2019-10-01 Impact factor: 4.497